Hi Hady,

Thanks for the question. This is definitely a very common practice in Reinforcement Learning. The idea is that the agent needs to explore in order to get a good understanding of the reward dynamics of the environment it is in. By sometimes acting randomly, it can end up in states it may know less about, and can learn from them to obtain a more optimal policy.

PhD. Interests include Deep (Reinforcement) Learning, Computational Neuroscience, and Phenomenology.

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